progeny.Niche: Performs offspring, crossover, mutation, and elitism...
In galgo: Genetic Algorithms for Multivariate Statistical Models from Large-Scale Functional Genomics Data

Description Usage Arguments Details Value Author(s) References See Also Examples

Performs offspring, crossover, mutation, and elitism mechanism to generate the “evolved” niche.

1 2	## S3 method for class 'Niche' progeny(ni, immigration=NULL, ...)

immigration

Chromosomes wanted to immigrate (replacing) in the niche.

The basic idea to generate a progeny is a random selection biased toward the best chromosomes (see Goldberg). We implented this idea as a weighted probability for a chromosome to be selected using the formula:

p = scale * max(0,fitness - mean * mean(fitness))\^\ power

where scale, mean and power are the properties of the niche (offspringScaleFactor, offspringMeanFactor and offspringPowerFactor respectively). The default values were selected to be reasonably bias when the variance in the fitness are both high (at early generations) and low (in late generatios).

offspring is part of progeny method.

For related details For more information see Niche.

Returns nothing.

Victor Trevino. Francesco Falciani Group. University of Birmingham, U.K. http://www.bip.bham.ac.uk/bioinf

Goldberg, David E. 1989 Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Pub. Co. ISBN: 0201157675

For more information see Niche. *offspring(), *crossover().

   cr <- Chromosome(genes=newCollection(Gene(shape1=1, shape2=100),5))
   cr
   ni <- Niche(chromosomes = newRandomCollection(cr, 10))
   ni
   ni$fitness <- 1:10/10 # tricky fitness, only for showing purposes
   progeny(ni)
   ni